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Gene regulatory networks (GRNs)
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创建时间：
2024-09-18
相关数据集
Correction: Functional Translational Readthrough: A Systems Biology Perspective
Correction: Functional Translational Readthrough: A Systems Biology Perspective
NIAID Data Ecosystem80
Master regulators and cofactors of human neuronal cell fate specification identified by CRISPR gene activation screens [CAS_TF_sgASCL1_Screen_AmpliconSeq]. Master regulators and cofactors of human neuronal cell fate specification identified by CRISPR gene activation screens [CAS_TF_sgASCL1_Screen_AmpliconSeq]
Technologies to reprogram cell-type specification have revolutionized the fields of regenerative medicine and disease modeling. Currently, the selection of fate-determining factors for cell reprogramm
NIAID Data Ecosystem60
Additional file 1 of Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions
R code to reproduce the 1D example model output, discrepancy, emulation and history matching plots of Figs. 1, 2, 4, and 5 respectively. (R 11 kb)
DataCite Commons2020-08-31 更新50
Integrative regulatory network analysis results based on two network structure parameters (degree and betweenness centrality).
Integrative regulatory network analysis results based on two network structure parameters (degree and betweenness centrality).
Figshare2016-12-23 更新60
The independent and identical distribution model successfully predicts CL distributions.
Quality of fit was assessed by squared-error between the fit and observed CL distributions (column 2) as well as the Pearson correlation r of the fit and observed CL data (column 3). 17/18 samples sho
NIAID Data Ecosystem50
